Description: This presentation highlights the use of analytics for accelerated underwriting in the U.S. Life Insurance industry.

Description: This presentation offers an overview of enterprise risk management with topics covering benefits of risk management for an insurance company, establishing an ERM framework, risk reporting, and predictions about the future of ERM.

Description: Summary of the 2002 Member and Candidate Research Findings

Description: Insurance companies utilize derivatives in a variety of ways to manage and mitigate risks that are inherent in their investment or liability portfolios. They can be characterized by three main features: medium long-term duration; large volumes; and significant market risk exposure. Given the persistent low interest rate environment across the curve since the 2008 financial crisis, large companies need to hedge their liabilities, as illustrated by the significant recent increase from $786 billion as of fiscal year 2010 to $1,885 billion as of FY 2014 by insurance companies.As the Variable Annuities (VA) guarantees embedded within those liabilities hold a strong convex risk profile with respect to the underlying stock, VA carriers need to buy some convex hedge assets such as options to match the liability risk profile to improve hedge effectiveness, implying the growing cost of hedging. Such market impact of the quantity on the cost of hedging is further magnified in the recent period with the short-lived, but sharp transitions from low volatility to high volatility, when market volatility suddenly increases, even when there were no well-known fundamental catalysts. This partially stems from the growing managed volatility funds embedded within VA ($bn 400).VA carriers willing to trade a large quantity of options will see such market impact as an important dilemma, as the cost of placing one large order to close position will be far greater than the sum of infinitely small orders differed in time. In that context, managing market impact requires introducing an explicit market impact function, the influence of which the agent will try to minimize. The effect on hedge asset prices significantly depends on whether there is or not significant delta hedging activity. In case of no significant delta hedging activity, the risk appetite has a significant influence on the optimal execution strategy. If there is significant delta hedging activity, the optimal trading involve

Description: We will determine the role of risk function in evaluating operational risks associated with the implementation of LDTI and any system conversions. Are all models validated by the risk function before they are productionalized and results used by management to make business decisions? Does the risk function play a part in to assist finance, actuarial and investor relations in communicating changes and volatility resulting from LDTI? If not, should it? We will articulate the role that risk functions play in being part of large transformation from the start as a stakeholder/input provider to subsequently being an independent reviewer (i.e., second Line of defense).

Description: Natural Language Process (NLP) techniques can be used to find insights in large unstructured data sets and Word Embeddings are pervasive on a wide spectrum of NLP applications. These word representations improve downstream tasks such as Sentiment Analysis, Recommender System, and Topic Modeling. Learn how NLP transforms the Insurance Industry; and how word embeddings and pre-trained models improve the effectiveness and performance of NLP applications. NLP is increasing in importance for insurance companies. Additional application examples are available in session 36.

Description: Simple mathematical equations often outperform very complex ones in predictive analytics (PA) situations. This session will cover "why" and "how" to guard against overfitting and other potential pitfalls that might not be intuitive.

Description: Representing a large indexed annuity writer, the speakers will discuss the growth and evolution of the market for index annuity products. Moreover, the challenges that growth has introduced and how those challenges are being met. While much of the market consists of simple, accumulation-oriented S&P 500 based strategies with 0% return floors, a significant portion of industry sales come from more complex products with highly customized index strategies and other features, such as living benefit guarantees. Attendees will gain a broad understanding of the indexed annuity market, product and features, operational difficulties in risk management arising from product design, and how actuarial and hedge professionals are managing these issues.

Description: Interest rates steadily declining ever since the Volcker Fed tackled inflation in the early eighties. Ten years of stimulating monetary policy brought new lows. We should have braced ourselves for a return to neutrality, but that concept remains elusive. We will explore structural issues that have brought rates to these low levels and the potential impacts on the insurance business.

Description: 1) Vision of the applications and development of data analytics in life insurance industry in Asia; 2) How big is the pie and what are the current obstacles insurers are facing; 3) Utilization of data and analytics target operating models as a competitive advantage, with focus on future readiness, controls, efficiency and effectiveness; 4) Potential roadmap ahead: a) Assessment of the current state of maturity levels of data and analytics initiatives; b) Identification of technology and competency gaps; c) Establishment of data integration and management process within BUs, alignment of advanced analytics tools across departments, with centralized data governance, technology structure and decision making framework; d) Definition of criteria and metrics to measure key outcomes and business value added. 5) Case study on agency behavior management: a practical example on how data analytics is being used in practice: (a) key business objectives: improve agent behaviors and maximize productivity with the lowest possible compensation costs; (b) data collection and current state assessment; (c) detailed predictive modeling process, modeling outcomes, and business implications; (d) implementation and monitoring process.